13,457 research outputs found

    Employing dynamic fuzzy membership functions to assess environmental performance in the supplier selection process

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    The proposed system illustrates that logic fuzzy can be used to aid management in assessing a supplier's environmental performance in the supplier selection process. A user-centred hierarchical system employing scalable fuzzy membership functions implement human priorities in the supplier selection process, with particular focus on a supplier's environmental performance. Traditionally, when evaluating supplier performance, companies have considered criteria such as price, quality, flexibility, etc. These criteria are of varying importance to individual companies pertaining to their own specific objectives. However, with environmental pressures increasing, many companies have begun to give more attention to environmental issues and, in particular, to their suppliers’ environmental performance. The framework presented here was developed to introduce efficiently environmental criteria into the existing supplier selection process and to reflect on its relevant importance to individual companies. The system presented attempts to simulate the human preference given to particular supplier selection criteria with particular focus on environmental issues when considering supplier selection. The system considers environmental data from multiple aspects of a suppliers business, and based on the relevant impact this will have on a Buying Organization, a decision is reached on the suitability of the supplier. This enables a particular supplier's strengths and weaknesses to be considered as well as considering their significance and relevance to the Buying OrganizationPeer reviewe

    A hybrid and integrated approach to evaluate and prevent disasters

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    EU rural policy: proposal and application of an agricultural sustainability index

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    In this paper I propose an Agricultural Sustainability Index (ASI) starting from a ‘political’ perspective: European legislation in the rural sector. I try to answer these questions. How can we measure sustainability in agriculture? How do we measure the enhancement (if any) of the European policy for sustainability in agriculture? Why do some geographical areas perform better than others? Considering these questions, the paper suggests a model for measuring sustainability in agriculture and an approach to compare performances among different geographical contexts. The model puts together different dimensions of sustainability in agriculture, combining Geographical Information System (GIS) analysis and Multi-Criteria Analysis (MCA). Using eighteen agricultural indicators divided into three dimensions, social, economic and environmental, the model incorporates the following stages: (i) indicator specification and definition of the decisional framework; (ii) indicators' normalisation by means of transformation functions based on the fuzzy logic approach; (iii) indicators weighted by Analytic Hierarchy Process (AHP) techniques; (iv) indicators aggregated to obtain the ASI. The model is tested on a specific area: Alta Val d’Agri, a rural area in the southern Basilicata Region. Final results show that ASI consistently synthesises the evolution of thirty years of rural development policy.Agricultural sustainability, Indicators, GIS-MCA

    An empirical learning-based validation procedure for simulation workflow

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    Simulation workflow is a top-level model for the design and control of simulation process. It connects multiple simulation components with time and interaction restrictions to form a complete simulation system. Before the construction and evaluation of the component models, the validation of upper-layer simulation workflow is of the most importance in a simulation system. However, the methods especially for validating simulation workflow is very limit. Many of the existing validation techniques are domain-dependent with cumbersome questionnaire design and expert scoring. Therefore, this paper present an empirical learning-based validation procedure to implement a semi-automated evaluation for simulation workflow. First, representative features of general simulation workflow and their relations with validation indices are proposed. The calculation process of workflow credibility based on Analytic Hierarchy Process (AHP) is then introduced. In order to make full use of the historical data and implement more efficient validation, four learning algorithms, including back propagation neural network (BPNN), extreme learning machine (ELM), evolving new-neuron (eNFN) and fast incremental gaussian mixture model (FIGMN), are introduced for constructing the empirical relation between the workflow credibility and its features. A case study on a landing-process simulation workflow is established to test the feasibility of the proposed procedure. The experimental results also provide some useful overview of the state-of-the-art learning algorithms on the credibility evaluation of simulation models

    Hazard function deployment: a QFD-based tool for the assessment of working tasks–a practical study in the construction industry

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    Despite the efforts made, the number of accidents has not significantly decreased in the construction industry. The main reasons can be found in the peculiarities of working activities in this sector, where hazard analysis and safety management are more difficult than in other industries. To deal with these problems, a comprehensive approach for hazard analysis is needed, focusing on the activities in which a working task is articulated since they are characterized by different types of hazards and thus risk levels. The study proposes a methodology that integrates quality function deployment (QFD) and analytic network process methods to correlate working activities, hazardous events and possible consequences. This provides more effective decision-making, while reducing the ambiguity of the qualitative assessment criteria. The results achieved can augment knowledge on the usability of QFD in safety research, providing a basis for its application for further studies

    A comparison of multi-criteria methods for spare parts classification

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    Spare parts classification is a fundamental step in spare parts inventory management. Through classification, the parts are grouped using a set of relevant criteria. Methodologies and methods for multicriteria decision making are used to support the classification of spare parts. In this paper, a comparative study between the use of the multi-criteria classification based on rules and the multi-criteria classification using the Analytic Hierarchy Process is presented, showing the advantages and disadvantages of each method. The study confirmed that the multi-criteria method based on rules is more easily applied in organizations. The multi-criteria method using Analytic Hierarchy Process required more calculations, turning the implementation of the method more complicated, especially for non-Analytic Hierarchy Process specialists
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